mbImpute: an accurate and robust imputation method for microbiome data

A critical challenge in microbiome data analysis is the existence of many non-biological zeros, which distort taxon abundance distributions, complicate data analysis, and jeopardize the reliability of scientific discoveries. To address this issue, we propose the first imputation method for microbiom...

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Detalles Bibliográficos
Autores principales: Jiang, Ruochen, Li, Wei Vivian, Li, Jingyi Jessica
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8240317/
https://www.ncbi.nlm.nih.gov/pubmed/34183041
http://dx.doi.org/10.1186/s13059-021-02400-4
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author Jiang, Ruochen
Li, Wei Vivian
Li, Jingyi Jessica
author_facet Jiang, Ruochen
Li, Wei Vivian
Li, Jingyi Jessica
author_sort Jiang, Ruochen
collection PubMed
description A critical challenge in microbiome data analysis is the existence of many non-biological zeros, which distort taxon abundance distributions, complicate data analysis, and jeopardize the reliability of scientific discoveries. To address this issue, we propose the first imputation method for microbiome data—mbImpute—to identify and recover likely non-biological zeros by borrowing information jointly from similar samples, similar taxa, and optional metadata including sample covariates and taxon phylogeny. We demonstrate that mbImpute improves the power of identifying disease-related taxa from microbiome data of type 2 diabetes and colorectal cancer, and mbImpute preserves non-zero distributions of taxa abundances. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-021-02400-4).
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spelling pubmed-82403172021-06-30 mbImpute: an accurate and robust imputation method for microbiome data Jiang, Ruochen Li, Wei Vivian Li, Jingyi Jessica Genome Biol Method A critical challenge in microbiome data analysis is the existence of many non-biological zeros, which distort taxon abundance distributions, complicate data analysis, and jeopardize the reliability of scientific discoveries. To address this issue, we propose the first imputation method for microbiome data—mbImpute—to identify and recover likely non-biological zeros by borrowing information jointly from similar samples, similar taxa, and optional metadata including sample covariates and taxon phylogeny. We demonstrate that mbImpute improves the power of identifying disease-related taxa from microbiome data of type 2 diabetes and colorectal cancer, and mbImpute preserves non-zero distributions of taxa abundances. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-021-02400-4). BioMed Central 2021-06-28 /pmc/articles/PMC8240317/ /pubmed/34183041 http://dx.doi.org/10.1186/s13059-021-02400-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Jiang, Ruochen
Li, Wei Vivian
Li, Jingyi Jessica
mbImpute: an accurate and robust imputation method for microbiome data
title mbImpute: an accurate and robust imputation method for microbiome data
title_full mbImpute: an accurate and robust imputation method for microbiome data
title_fullStr mbImpute: an accurate and robust imputation method for microbiome data
title_full_unstemmed mbImpute: an accurate and robust imputation method for microbiome data
title_short mbImpute: an accurate and robust imputation method for microbiome data
title_sort mbimpute: an accurate and robust imputation method for microbiome data
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8240317/
https://www.ncbi.nlm.nih.gov/pubmed/34183041
http://dx.doi.org/10.1186/s13059-021-02400-4
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